A graph-based algorithm for RNA-seq data normalization
The use of RNA-sequencing has garnered much attention in the recent years for characterizing and understanding various biological systems. However, it remains a major challenge to gain insights from a large number of RNA-seq experiments collectively, due to the normalization problem. Current normalization methods are based on assumptions that fail to hold when RNA-seq profiles become more abundant and heterogeneous. We present a normalization procedure that does not rely on these assumptions, or on prior knowledge about the reference transcripts in those conditions. This algorithm is based on a graph constructed from intrinsic correlations among RNA-seq transcripts and seeks to identify a set of densely connected vertices as references. Application of this algorithm on our benchmark data showed that it can recover the reference transcripts with high precision, thus resulting in high-quality normalization. As demonstrated on a real data set, this algorithm gives good results and is efficient enough to be applicable to real-life data.\n\n2012 ACM Subject ClassificationApplied computing [->] Computational transcriptomics, Applied computing [->] Bioinformatics\n\nDigital Object Identifier10.4230/LIPIcs.WABI.2018.xxx\n\nFundingThis material was based on research supported by the National Heart, Lung, and Blood Institute (NHLBI)-NIH sponsored Programs of Excellence in Glycosciences [grant number HL107152 to B.K.], and partially by NSF [CAREER grant 1350344 to M.M.]. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon.